Video Event Recognition Using Conditional Random Fields

Abstract

Event Classification in videos is a challenging task in computer vision based systems. The Crowd Event Classification system recognizes a large number of video events. The decisive of the model is a difficult task in the event classification. a more important role in the various research fields particularly in surveillance detection system. In the existing system it is done by using Deep Hierarchical Context Model which utilizes the contextual information from the feature extraction and prior level recognition of event in video. However, this research method might perform low with increased volume of videos and might failed to predict the events accurately with less interrelation contextual features. The new method namely Improved Hybridized Deep Structured Model (IHDSM) resolve the above problem. Here,introduce three different context features that describe neighborhood event. Here the Hybrid textual perceptual descriptor and concept based attribute extraction is performed for accurate recognition of video events. These extracted interaction context features are grouped by using improved k means algorithm. And then utilize the proposed improved deep structured model that combines convolutional neural networks (CNNs) and Conditional Random Fields (CRFs) to learn the middle level representationsand combine the bottom feature level, middle semanticlevel and top prior level contexts together for event recognition. This proposed research method is evaluated by using VIRAT data set whose simulation analysis is performed using matlab simulation toolkit. The overall evaluation of the proposed research method proves that the proposed method can provide better performance in terms of accurate recognition of events.